Optimal filter approximation by means of a phase-only filter with quantization

Kober, Vitaly; Yaroslavsky, Leonid P.; Campos, Juan; Yzuel, María Josefa

doi:10.1364/ol.19.000978

Cited by 20 publications

(11 citation statements)

References 4 publications

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“…18 It was later used for quantisation of Fourier spectra for digital hologram applications 19 and has also been used in the design of an optimally quantised phase-only matched filter for image recognition. 20 We compare our results with those obtained previously using uniform and a nonuniform quantisation technique, the popular k-means clustering algorithm, on our digital hologram data. Compression noise or artifacts that emerge in the decompressed hologram are not as big a concern for us as the affect of compression losses on our reconstructed object, range of viewing angles, and so on.…”

Section: Introductionmentioning

confidence: 83%

Optimal quantisation applied to digital holographic data

Shortt

Naughton

Javidi³

2005

SPIE Proceedings

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Digital holography is an inherently three-dimensional (3D) technique for the capture of real-world objects. Many existing 3D imaging and processing techniques are based on the explicit combination of several 2D perspectives (or light stripes, etc.) through digital image processing. The advantage of recording a hologram is that multiple 2D perspectives can be optically combined in parallel, and in a constant number of steps independent of the hologram size. Although holography and its capabilities have been known for many decades, it is only very recently that digital holography has been practically investigated due to the recent development of megapixel digital sensors with sufficient spatial resolution and dynamic range. The applications of digital holography could include 3D television, virtual reality, and medical imaging. If these applications are realised, compression standards will have to be defined. We outline the techniques that have been proposed to date for the compression of digital hologram data and show that they are comparable to the performance of what in communication theory is known as optimal signal quantisation. We adapt the optimal signal quantisation technique to complex-valued 2D signals. The technique relies on knowledge of the histograms of real and imaginary values in the digital holograms. Our digital holograms of 3D objects are captured using phase-shift interferometry.

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Section: Introductionmentioning

confidence: 83%

Optimal quantisation applied to digital holographic data

Shortt

Naughton

Javidi³

2005

SPIE Proceedings

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“…Further accurate estimation of the target location with correlation filters can be carried out. 5,6 Negative values of the DC indicate that a tested filter fails to recognize the target. Assume that an input image f(x, y) contains the input objects s(x, y) (desired and nondesired) and the nonoverlapping background b(x, y):…”

Section: A Classical Joint Transform Correlatormentioning

confidence: 99%

“…4 Some of these measures can essentially be improved using an adaptive approach to the filter design. 5,6 According to this concept, we are interested in a filter with good performance characteristics for a given observed scene, i.e., with a fixed set of patterns or a fixed background to be rejected, rather than in a filter with average performance parameters over an ensemble of images. The response of correlation filters also depends on the scale, orientation, and any deformation in the input object.…”

Section: Introductionmentioning

confidence: 99%

Pattern recognition with an adaptive joint transform correlator

2006

Self Cite

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An adaptive joint transform correlator for real-time pattern recognition is presented. A reference image for the correlator is generated with a new iterative algorithm. The training algorithm is based on synthetic discriminant functions. The obtained reference image contains the information needed to reliably discriminate a target against known false objects and a cluttered background. Calibration lookup tables of all optoelectronics elements used are included in the design of the adaptive joint transform correlator. Two methods for the implementation of the proposed joint transform correlator in an optodigital setup are considered. Experimental results are provided and compared with those of computer simulations.

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“…The drawback of the POF is its poor discrimination capability for a low-contrast target embedded into a complicated background scene. An approximation of the OF by means of phase-only filters with a quantization was made (Kober et al, 1994). There, the approximate filters with high light efficiency and discrimination capability close to that of the OF were suggested.…”

Section: Introductionmentioning

confidence: 99%